Cluster analysis: a useful technique to identify elderly cardiac patients at risk for poor quality of life

Abstract

Objective: The purposes of this study are (1) to examine the frequency of cardiac symptoms in elderly people one year after acute myocardial infarction (AMI) and/or coronary artery bypass surgery (CABG); (2) to identify patient subgroups (cluster solutions) based on cardiac symptoms after cardiac events and (3) to determine if these subgroups vary based on health related quality of life and psychological distress.

Methods: A sample of 206 elderly, unpartnered, patients (age > or = 65) were interviewed one year after AMI and/or CABG by telephone. Cardiac symptoms, SF-36, POMS, and QOL-I were measured. A hierarchical cluster analysis was used to identify patient subgroups based on cardiac symptoms, using a combination of dendrograms and stopping rules.

Results: Three subgroups were identified: (1) the Weary (19.4%), (2) the Diffuse symptom (68.4%), and (3) the Breathless groups (12.2%). The Weary group had significantly lower scores on all of SF-36 subscales (except for social functioning) and higher scores on all of POMS subscales (except for Anger/hostility and Confusion/Bewilderment) compared to the Diffuse symptom group.

Conclusions: The cluster analysis was useful to identify the subgroup with poorer recovery. Patients in the Weary group need more attention and intervention strategies to improve their health.